Building lightweight intrusion detection system based on random forest

被引：0

作者：

Kim, Dong Seong ^{[1
]}

Lee, Sang Min ^{[1
]}

Park, Jong Sou ^{[1
]}

机构：

[1] Hankuk Aviat Univ, Network Secur Lab, Dept Comp Engn, Goyang City 412791, Gyeonggi Do, South Korea

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS | 2006年 / 3973卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a new approach to build lightweight Intrusion Detection System (IDS) based on Random Forest (RF). RF is a special kind of ensemble learning techniques and it turns out to perform very well compared to other classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN). In addition, RF produces a measure of importance of feature variables. Our approach is able not only to show high detection rates but also to figure out stable output of important features simultaneously. The results of experiments on KDD 1999 intrusion detection dataset indicate the feasibility of our approach.

引用

页码：224 / 230

页数：7

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